Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
propagator-j2
by blitheli计算考虑 J2 项摄动的轨道轨迹,输出 CzmlPositionOut(CZML结构的位置序列)。当用户需要 J2 摄动、地球扁率影响、一阶带谐项轨道递推时使用。
lambert
by blitheli求解 Lambert 问题(始末位置速度已知,单圈转移),输出起点和终点的速度增量(DV1、DV2)。当用户需要根据始末状态向量和飞行时间计算轨道转移速度增量时使用。支持多个Lambert转移算例同时计算。
satellite-imagery-query
by blitheli按地点/区域, 时间, 分辨率意图与光学或 SAR 类型查询并下载卫星影像时使用本仓库的 Python CLI 与配置约定.
celestial-transfer
by blitheli行星与小行星之间的 Lambert 转移轨道计算。出发/到达天体可为行星(Earth,Mars,Ceres 等)或小行星(MPC 编号/名称);小行星可传入历元轨道根数以避免 MPC 网络查询。用户需要日心系转移窗口与 Delta-V 时使用。
orbitwizard-geo
by blitheli生成地球同步轨道(GEO)参数。用户需要根据定点经度和倾角快速生成 GEO 初始轨道时使用。
orbitwizard-molniya
by blitheli生成莫尔尼亚轨道参数。用户需要根据近地点高度、远地点经度等参数快速生成 Molniya 初始轨道时使用。
orbitwizard-sso
by blitheli生成太阳同步轨道(SSO)参数。用户需要根据轨道高度与降交点地方时快速生成 SSO 初始轨道时使用。
orbitwizard-walker
by blitheli生成 Walker 星座参数。用户需要根据种子轨道与星座构型参数快速生成 Walker 星座时使用。
propagator-hpop
by blitheli通过 Astrox WebAPI 的 POST /Propagator/HPOP 计算高精度轨道递推(HPOP)轨迹,考虑多种摄动力,输出 CzmlPositionOut(CZML结构的位置序列)。当用户需要高精度轨道计算、考虑多种摄动因素、HPOP 时使用。
propagator-simple-ascent
by blitheli通过 Astrox WebAPI 的 POST /Propagator/SimpleAscent 计算火箭主动上升段轨迹,输出 CzmlPositionOut(CZML结构的位置序列)。当用户需要主动上升段、火箭上升、发射轨迹计算时使用。
query-facility
by blitheli从数据库中获取所有符合查询条件的地面站。当用户需要查询测控站、地面站、设施信息时使用。
access
by blitheli计算两对象间可见性/访问弧段。当用户需要测站对卫星可见窗口、卫星与卫星之间Access弧段、AER 采样时使用。
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.